Air Quality Assessment by Statistical Learning-Based Regularization

Abstract

Öz: PM10 can be stated as a particulate matter smaller than 10 micrometer and it can be suspended in the air. The incremental concentration of PM10 affects both human and environment drastically. In this study, an air quality assessment by exhibiting the potential relationships among the secondary indicators and PM10 has been focused. For the analyses, statistical learning-based regularization procedures such as Ridge, the Lasso and Elastic-net algorithms have been practiced. In particular, use of Elastic-net algorithm in predicting PM10 concentration includes a novelty. As a result of the computational studies, it has been recorded that all the models showed high accuracy capacities. However, the elastic-net model outperforms the other models both accuracy and robustness (stability). Considering the errormeasurements (MSE and MAPE), the best numerical results have been provided by the Elastic-net model. Use of machine learning-based regularization algorithms in environmental problems can provide accurate model structures as well as generality and transparency.